Choosing an open model for regulated AI

Capability rankings move fast and this is not legal advice. Licensing notes are accurate as of publication but always verify the current terms directly. Work with your counsel on compliance obligations.

Five criteria for regulated teams

A model evaluation for a regulated use case needs more dimensions than a standard benchmark leaderboard. Here is what matters when your legal and security teams have a vote:

• Capability. Does the model perform on your actual tasks — not just MMLU, but the reasoning, summarization, or code generation your users need?
• License terms. MIT and Apache 2.0 are broadly permissive. The Llama 4 Community License restricts use above 700 million monthly active users. Know which camp your organization falls into.
• Context length. Longer contexts mean fewer chunking hacks, but they also consume KV-cache memory fast. Match the window to your documents, not to the marketing number.
• Provenance. Where was the model trained? By whom? On what data? Regulated procurement increasingly cares about supply-chain transparency.
• Deployability. Can the model run on the hardware and topology your compliance requires — in your VPC, on dedicated GPUs, under your control?

The models on the table

HyperInfer serves a range of open-weight models on dedicated single-tenant GPUs. Here are seven of the flagships and what you need to know about each.

DeepSeek V4 Pro is a 1.6T-parameter mixture-of-experts model with a 1 million token context window. It is the heaviest reasoning model in the lineup, released under an MIT license. DeepSeek V4 Flash is the speed-focused sibling: a 284B-parameter MoE under the same MIT license, designed for high-throughput production workloads.

Qwen 3.5 397B A17B is a 397B total, 17B active MoE model released under Apache 2.0 with roughly 262K token context. It is the most permissively licensed top-tier model available and the strongest Apache 2.0 option for teams that cannot accept the Llama Community License's MAU cap. Llama 4 Maverick runs 400B total with 17B active parameters across 128 experts. It is competitive with Qwen on capability but subject to the Llama 4 Community License, which is free for organizations under 700 million monthly active users. GLM 5.2 is a 744B-parameter MoE model with a 1 million token context window, MIT licensed. Its architecture gives it a distinct reasoning profile that some teams prefer for analysis-heavy workloads. Gemma 4 (Apache 2.0) and Mistral Large 3 (675B total, 41B active MoE, 256K context, Apache 2.0) round out the lineup with strong general-purpose capability under permissive terms.

Why the license terms actually matter

MIT and Apache 2.0 are the gold standard for enterprise adoption: no usage restrictions, no MAU caps, no downstream obligations beyond attribution and patent grants. The Llama 4 Community License is more nuanced. It is free for most organizations but caps usage at 700 million monthly active users. If your product serves a large consumer base or is deployed inside a platform with significant traffic, that cap may apply to you. The distinction matters because a license restriction you discover after building your pipeline around a model is a migration you did not budget for. For teams that want zero licensing uncertainty, the migration from proprietary APIs to open weights is easier when you pick an MIT or Apache 2.0 model from the start.

Provenance, sovereignty, and where the model runs

A growing number of regulated procurement processes ask not just what a model can do, but who built it, where it was trained, and where inference happens. Open-weight models give you an answer to all three because the weights are inspectable and the deployment location is yours to choose. Every model above can be deployed on dedicated GPUs inside your VPC, running in the region you specify, with zero data retention. The model does not phone home. The provider does not see your prompts. That architectural answer satisfies more compliance questionnaires than any benchmark score ever will.

For teams operating under the EU AI Act, GDPR, or similar frameworks, the combination of a permissive license and private deployment addresses the two biggest regulatory risks: data residency and usage-rights ambiguity. You can see the full set of models served on dedicated infrastructure here.